<p><b>Presents </b><b>recent advances in both models and systems for intelligent decision making.</b></p><p>Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have
Multicriteria Decision Aid and Artificial Intelligence
- Publisher
- Wiley-Blackwell
- Year
- 2013
- Tongue
- English
- Leaves
- 353
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Presents recent advances in both models and systems for intelligent decision making.
Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems.
The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering.
Multicriteria Decision Aid and Artificial Intelligence:
- Covers all of the recent advances in intelligent decision making.
- Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems.
- Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments.
- Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications.
- Is written by experts in the field.
This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial.Content:
Chapter 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview (pages 1β23): Michael Doumpos and Constantin Zopounidis
Chapter 2 Intelligent Decision Support Systems (pages 25β44): Gloria Phillips?Wren
Chapter 3 Designing Distributed Multi?Criteria Decision Support Systems for Complex and Uncertain Situations (pages 45β76): Tina Comes, Niek Wijngaards and Frank Schultmann
Chapter 4 Preference Representation with Ontologies (pages 77β99): Aida Valls, Antonio Moreno and Joan Borras
Chapter 5 Neural Networks in Multicriteria Decision Support (pages 101β126): Thomas Hanne
Chapter 6 Rule?Based Approach to Multicriteria Ranking (pages 127β160): Marcin Szela?g, Salvatore Greco and Roman Slowinski
Chapter 7 About the Application of Evidence Theory in Multicriteria Decision Aid (pages 161β187): Mohamed Ayman Boujelben and Yves De Smet
Chapter 8 Interactive Approaches Applied to Multiobjective Evolutionary Algorithms (pages 189β207): Antonio Lopez Jaimes and Carlos A. Coello Coello
Chapter 9 Generalized Data Envelopment Analysis and Computational Intelligence in Multiple Criteria Decision Making (pages 209β233): Yeboon Yun and Hirotaka Nakayama
Chapter 10 Fuzzy Multiobjective Optimization (pages 235β271): Masatoshi Sakawa
Chapter 11 Multiple Criteria Decision Aid and Agents: Supporting Effective Resource Federation in Virtual Organizations (pages 273β284): Pavlos Delias and Nikolaos Matsatsinis
Chapter 12 Fuzzy Analytic Hierarchy Process Using Type?2 Fuzzy Sets: An Application to Warehouse Location Selection (pages 285β308): Irem Ucal Sary, Basar Oztaysi and Cengiz Kahraman
Chapter 13 Applying Genetic Algorithms to Optimize Energy Efficiency in Buildings (pages 309β333): Christina Diakaki and Evangelos Grigoroudis
Chapter 14 Nature?Inspired Intelligence for Pareto Optimality Analysis in Portfolio Optimization (pages 335β345): Vassilios Vassiliadis and Georgios Dounias
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